A New Approach for Semantic Web Matching

In this work we propose a new approach for semantic web matching to improve the performance of Web Service replacement. Because in automatic systems we should ensure the self-healing, self-configuration, self-optimization and self-management, all services should be always available and if one of them crashes, it should be replaced with the most similar one. Candidate services are advertised in Universal Description, Discovery and Integration (UDDI) all in Web Ontology Language (OWL). By the help of bipartite graph, we did the matching between the crashed service and a Candidate one. Then we chose the best service, which had the maximum rate of matching. In fact we compare two services` functionalities and capabilities to see how much they match. We found that the best way for matching two web services, is comparing the functionalities of them.

A Three Phase Semantic Web Matchmaker

Since using environments that are made according to the service oriented architecture, we have more effective and dynamic applications. Semantic matchmaking process is finding valuable service candidates for substitution. It is a very important aspect of using semantic Web Services. Our proposed matchmaker algorithm performs semantic matching of Web Services on the basis of input and output descriptions of semantic Web Services matching. This technique takes advantages from a graph structure and flow networks. Our novel approach is assigning matchmaking scores to semantics of the inputs and outputs parameters and their types. It makes a flow network in which the weights of the edges are these scores, using FordFulkerson algorithm, we find matching rate of two web services. So, all services should be described in the same Ontology Web Language. Among these candidates, best one is chosen for substitution in the case of an execution failure. Our approach uses the algorithm that has the least running time among all others that can be used for bipartite matching. The importance of problem is that in real systems, many fundamental problems will occur by late answering. So system`s service should always be on and if one of them crashes, it would be replaced fast. Semantic web matchmaker eases this process.

How to Discover a Semantic Web Service by Knowing Its Functionality Parameters

In this work, we show how to discover a semantic web service among a repository of web services. A new approach for web service discovery based on calculating the functions similarity. We define the Web service functions with Ontology Web Language (OWL). We wrote some rules for comparing two web services` parameters. Our algorithm compares the parameters of two web services` inputs/outputs by making a bipartite graph. We compute the similarity rate by using the Ford-Fulkerson algorithm. The higher the similarity, the less are the differences between their functions. At last, our algorithm chooses the service which has the highest similarity. As a consequence, our method is useful when we need to find a web service suitable to replace an existing one that has failed. Especially in autonomic systems, this situation is very common and important since we need to ensure the availability of the application which is based on the failed web service. We use Universal Description, Discovery and Integration (UDDI) compliant web service registry.

Study of Some Recent Crossovers Effects on Speed and Accuracy of Genetic Algorithm, Using Symmetric Travelling Salesman Problem

The Travelling Salesman Problem (TSP) is one of the most famous optimization problems. The Genetic Algorithm (GA) is one of metaheuristics that have been applied to TSP. The Crossover and mutation operators are two important elements of GA. There are many TSP solver crossover operators. In this paper, we state implementation of some recent TSP solver crossovers at first and then we use each of them in GA to solve some Symmetric TSP (STSP) instances and finally compare their effects on speed and accuracy of presented GA.

Ontology Enrichment by Extracting Hidden Assertional Knowledge from Text

In this position paper we present a new approach for discovering some special classes of assertional knowledge in the text by using large RDF repositories, resulting in the extraction of new non-taxonomic ontological relations. Also we use inductive reasoning beside our approach to make it outperform. Then, we prepare a case study by applying our approach on sample data and illustrate the soundness of our proposed approach. Moreover in our point of view current LOD cloud is not a suitable base for our proposal in all informational domains. Therefore we figure out some directions based on prior works to enrich datasets of Linked Data by using web mining. The result of such enrichment can be reused for further relation extraction and ontology enrichment from unstructured free text documents.

Developing Improved Greedy Crossover to Solve Symmetric Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is one of the most famous optimization problems. Greedy crossover designed by Greffenstette et al, can be used while Symmetric TSP (STSP) is resolved by Genetic Algorithm (GA). Researchers have proposed several versions of greedy crossover. Here we propose improved version of it. We compare our greedy crossover with some of recent crossovers, we use our greedy crossover and some recent crossovers in GA then compare crossovers on speed and accuracy.